We developed a relative risk model for projecting breast cancer risk that includes mammographic density, weight, family history, age at first live birth and number of previous breast biopsies. The model has modestly higher discriminatory power than the "Gail model" that does not include mammographic density. The data used presented a number of challenges, because mammographic density measurements were only available on subsets of the study population. This work is in press and a related paper on projecting absolute risk using mammographic density has been published. We published a model for projecting the risk of breast cancer for African American women based on data from the Cancer and Reproductive Experiences Study and SEER rates. This model usually produces higher risk projections than NCI"s current Breast Cancer Risk Assessment Tool for women aged 50 and older, but somewhat lower risks in young women. This work includes assessment of the validity of the model with independent data from the Women"s Health Initiative. We investigated whether seven recently identified common single nucleotide polymorphisms(SNPs) associated with breast cancer risk could improve the discriminatory accuracy of NCI"s Breast Cancer Risk Assessment Tool, which is based on simple questionnaire data. Adding the seven SNPs to the model increased discriminatory accuracy, measured as the area under the ROC curve, from 0.607 to 0.632, which is a smaller increase than adding mammographic density provides. We calculated that hundreds of SNPs would be needed to achieve high discriminatory accuracy. We addressed whether a woman from a high risk family known to carry mutations in BRCA1 or BRCA2 genes had above average risk of breast cancer even if she was found not to carry a mutation. Because most of the familial correlation in breast cancer risk is not due to BRCA1 or BRCA2 mutations, and because most high risk families are ascertained because several members are affected, there is reason to believe that such a woman remains at higher risk than the general population, even though the risk is not as high as for a mutation carrier. Studies are in progress to quantify the extent of such risk. We reviewed designs and analytic methods for estimating absolute risk from genetic mutations and compared risks estimated from population-based versus family studies. Relative risks from family studies can be higher than from population-based studies because within family comparisons are not attenuated by random familial effects. A paper is in press describing absolute risk models for proximal and distal colon cancer and for rectal cancer. These models produce an absolute risk for the earliest of proximal colon cancer, distal colon cancer and rectal cancer. We also assessed the validity of the models using independent data from the AARP Cohort and found the models to be well calibrated; this work is also in press.